Getting started with Julia#
You can run the PlantSimEngine tutorials without knowing all of Julia. Start by copying a complete example, then change one parameter and compare the result. This page explains a few patterns you will see along the way.
New to programming#
The Julia getting-started guide explains how to start Julia and enter commands. If you prefer a video course, Julia Programming for Nervous Beginners is aimed at people with no programming experience.
Installing packages and setting up an environment#
Follow Installing PlantSimEngine to create a project folder and install the tutorial packages. A project environment records which packages and versions your simulation uses.
Essential Julia concepts for PlantSimEngine#
The first tutorials mainly use these patterns:
| Code | Meaning |
|---|---|
lai = 2.0 | Store a value under the name lai |
Beer(0.6) | Create a Beer model with an extinction coefficient of 0.6 |
run!(model; steps=30) | Run a function, with the named option steps=30 |
(LAI=2.0, TT=12.0) | Group named values in a named tuple |
[1.0, 2.0, 3.0] | Create an array of three values |
values[1] | Read the first array entry; Julia indexing starts at 1 |
state.LAI | Read the value named LAI from state |
values .* 2 | Multiply every array entry by 2 |
Options such as steps=30 are called keyword arguments. The semicolon separates these named options from the other arguments. A dot before an operator, as in .*, applies the operation to each array entry. Julia calls this broadcasting.
A function name ending in !, such as run! or step!, usually means that the function changes something it was given. Here, running a simulation updates its objects' values.
When you start writing models, you will also meet types and methods. A type describes a kind of value; a model type can store its parameters. A method is a version of a function for particular types of arguments. The first model tutorial introduces these ideas with a complete equation and its parameters.
Cheatsheets#
The Julia Data Science basics cover common syntax and working with tables. There are also cheatsheets and a short introductory notebook.
Troubleshooting#
Ask Julia language questions on Julia Discourse. For errors from PlantSimEngine, use the common errors guide.
If you know R, Python, or MATLAB, Julia's comparison with other languages explains differences you may encounter. You can read about more advanced features, such as type promotion and parametric types, when a model needs them.
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